Question

我使用了以下代码。当我针对某些时期（例如100）运行它时，出现此错误，精度为0.0，但在这些时期中，我没有任何错误： /home/n/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1143：UndefinedMetricWarning：精度和F分数定义不明确，在没有可预测样本的标签中设置为0.0。 'precision'，'predicted'，平均值，warn_for） /home/n/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1143：UndefinedMetricWarning：精度和F得分定义不明确，由于没有可预测的样本，因此设置为0.0。 'precision'，'predicted'，平均值，warn_for） /home/n/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1143：UndefinedMetricWarning：精度和F分数定义不明确，在没有可预测标签的样本中设置为0.0。 'precision'，'predicted'，average，warn_for）

 precision    recall  f1-score   support

       0       0.00      0.00      0.00        46
       1       0.00      0.00      0.00        46
       2       0.00      0.00      0.00        46
       3       0.00      0.00      0.00        46
       4       0.00      0.00      0.00        46
       5       0.00      0.00      0.00        46

    micro avg       0.00      0.00      0.00       276
    macro avg       0.00      0.00      0.00       276
 weighted avg       0.00      0.00      0.00       276
  samples avg       0.00      0.00      0.00       276

我该如何解决？

我的代码：

from pathlib import Path
import glob
import cv2
import numpy as np
from keras.utils import np_utils
from keras.models import Model
from keras import layers
import keras
from sklearn.metrics import classification_report
import matplotlib.pyplot as plt



path_spec_train = "/home/n/project/train_6/"
spec_train = glob.glob(path_spec_train + "**/*.png") 
spec_train.sort()

X_modify = []
width = 200
height = 100
for spec in spec_train:
    specs = cv2.imread(spec)
    specs = cv2.cvtColor(specs,cv2.COLOR_BGR2GRAY)
    specs = cv2.resize(specs ,(width, height))
    specs = specs / np.max(specs)
    specs = specs.astype(np.float32)
    X_modify.append(specs)

X_train = np.asarray(X_modify,dtype=np.float32)
X_train = np.expand_dims(X_train, axis=3)




path_spec_test = "/home/n/project/test_6/"
spec_test = glob.glob(path_spec_test + "**/*.png") 
spec_test.sort()

X_modify_t = []
width = 200
height = 100
for spec_t in spec_test:
    specs_test = cv2.imread(spec_t)
    specs_test = cv2.cvtColor(specs_test,cv2.COLOR_BGR2GRAY)
    specs_test = cv2.resize(specs_test ,(width, height))
    specs_test = specs_test / np.max(specs_test)
    specs_test = specs_test.astype(np.float32)
    X_modify_t.append(specs_test)

X_test = np.asarray(X_modify_t,dtype=np.float32)
X_test = np.expand_dims(X_test, axis=3)

#load label

spk_ID = [wavs[i].split('/')[-1].lower() for i in range(number_of_files)]
spk_ID_t = [wavs_t[i].split('/')[-1].lower() for i in range(number_of_files_t)]
label_no = [spk_ID[i].split('_')[-2] for i in range(number_of_files)]
Y_train = np_utils.to_categorical(label_no)
label_no_t = [spk_ID_t[i].split('_')[-2] for i in range(number_of_files_t)]
Y_test = np_utils.to_categorical(label_no_t)


# Create my model
myinput = layers.Input(shape=(100,200,1))
conv1 = layers.Conv2D(64, 3, activation='relu', padding='same', strides=2)(myinput)
conv2 = layers.Conv2D(32, 3, activation='relu', padding='same', strides=2)(conv1)
flat = layers.Flatten()(conv2)
out_layer = layers.Dense(6, activation='softmax')(flat)

mymodel = Model(myinput, out_layer)
mymodel.summary()
mymodel.compile(optimizer=keras.optimizers.Adam(), loss=keras.losses.categorical_crossentropy, metrics=['accuracy'])

# train my model
network_history = mymodel.fit(X_train, Y_train, batch_size=128, epochs=100)


# acc per class
pred = np.round(mymodel.predict(X_test))
print(classification_report(Y_test, pred))

如何解决定义不正确的指标错误？

0 个答案: